改进自适应遗传算法的结构优化设计

Design of Structure Optimization Based on Adaptive Genetic Algorithm

介绍了离散变量的结构优化设计方法——遗传算法(Genetic Algorithms)的来源和运行参数。考虑到遗传算法在运算过程中表现出的缺点以及交叉率和变异率的选取对遗传算法的搜索能力和搜索效果的影响,同时为了提高遗传算法的收敛性,避免发生早熟收敛,对遗传算法进行了改进,引入一种基于个体适应度值的自适应遗传算法。并通过算例表明这种改进自适应遗传算法较基本遗传算法是更有效的,提高了算法的运行效率和计算精度。

The origin and parameters of the simple genetic algorithm （GA） were presented for optimizing the structural systems with discrete variables. In order to improve the convergence of GA and avoid the premature convergence, considering that the different choices of crossover and mutation will influence GA on search ability and effect, an adaptive GA was introduced. To the effect hereon, the adaptive GA was shown more effective than GA by an example, the computation precision and operating efficiency were all enhanced.